Dynamic Testing & Sandbox Validation

Static analysis has hard limits. An AI can read a piece of code and logically deduce that it should work, but until that code is executed, it remains a hypothesis.

A critical failure mode of AI-generated code suggestions is that they often compile perfectly but break the interface, violate an API contract, or fail under specific runtime conditions.

Beyond Static Analysis

The 2026 baseline demands that AI reviewers move beyond static text analysis and enter the realm of Dynamic Validation.

Before an AI confidently suggests a complex refactor or approves a high-risk Pull Request, it must be able to prove that its assumptions hold up at runtime.

The Execution Standard

1. Preview Environments: The AI must be capable of interacting with ephemeral preview environments (e.g., Vercel Previews, temporary Docker containers).
2. Automated Test Generation: If the AI suggests a fix, it must also generate the unit test that proves the fix works. A suggestion without a verifying test is incomplete.
3. Chaos Testing: For critical infrastructure changes, the AI should be able to simulate edge cases—network latency, malformed JSON payloads, null pointers—against the sandbox environment to ensure the new code handles failures gracefully.

Don’t trust an AI that only reads code. Trust an AI that can run it.